Biology subjects

Crandall, K. A.

Publications and source records attributed to Crandall, K. A..

At least 19 recordsLinked to original sources

bioRxiv2026

Mobile DNA Activity in Parkinson's Disease: A Locus-Specific View of Endogenous Retroviruses

Human endogenous retroviruses (HERVs) are mobile genetic sequences derived from ancient retroviral infections. While typically silenced, their reactivation has been implicated in gene dysregulation, aging, and immune-related transcriptional pathogenesis of some neurodegenerative diseases. Parkinsons disease (PD) is the second most common neurodegenerative disorder, yet its etiology and HERV reactivation remain poorly understood. This study investigates locus-specific HERV expression in early-stage PD, including genetic and non-genetic cases (all PD), idiopathic PD without a known genetic cause (iPD), and PD driven by leucine-rich repeat kinase 2 mutations (LRRK2 PD). We analyzed RNA-seq whole-blood samples from 492 individuals (358 all PD, 256 were iPD, 63 LRRK2 PD, and 134 healthy controls (HC)). We identified 20 significantly dysregulated HERV loci in all PD versus HC. Five HERV loci were shared with iPD analysis, and one HERV locus was shared with LRRK2 PD. Notably, these shared loci included HERV-H and ERVLE elements, indicating robust disease-associated retroviral signals independent of disease subtype. We found that genes proximal to these HERVs revealed pathways implicated in PD pathogenesis. Immune cell deconvolution showed increased neutrophil abundance and decreased resting CD4+ memory T cells proportions across the PD cohorts when compared to HC, consistent with neutrophil-lymphocyte ratio observed in previous peripheral immunity studies. Transcriptomic HERV alterations are present in whole blood across PD populations and are associated with dysregulation of fundamental cellular pathways and peripheral immune remodeling. Our findings motivate experimental validation of locus-specific HERV expression as a candidate blood-based signature with potential to inform PD neuroinflammatory and neurodegenerative processes.

bioinformatics↗

bioRxiv2026

Guided tokenization and domain knowledge enhance genomic language models' performance

Adapting language models to genomic and metagenomic sequences presents unique challenges, particularly in tokenization and task-specific generalization. Standard methods, such as fixed-length k-mers or byte pair encoding, often fail to preserve biologically meaningful patterns essential for downstream tasks. We introduce Guided Tokenization (GT), a strategy that prioritizes biologically and statistically important subsequences based on importance scores, model attention, and class distributions. Combined with domain adaptation, which incorporates prior domain specific biological knowledge, this approach improves both representation quality and classification accuracy in compact genomic language models (gLMs). GT enhances biological awareness in genomic language models, particularly for effective small and mid-sized models across key tasks, including DNA sequence read classification, promoter detection, antimicrobial resistance classification, and targeted amplicon taxonomic profiling. Our results highlight the promise of guided tokenization and domain-aware modeling for building efficient, biologically grounded language models for scalable genomic applications.

bioinformatics↗

bioRxiv2025

Curating 16S rRNA databases enhances taxonomic accuracy and computational efficiency in microbial profiling

The 16S rRNA gene serves as the gold standard molecular marker for microbial profiling, yet taxonomic assignment accuracy depends critically on reference database quality. Substantial heterogeneity exists among databases in sequence coverage, curation standards, and taxonomic nomenclature, leading to conflicting taxonomic assignments. Despite previous comparisons highlighting performance differences, the impact of database preprocessing, including sequence cleaning and redundancy removal, on taxonomic classification remains understudied. To improve database quality, we implemented novel cleaning approaches to remove nested sequences, duplicate sequences, and correct missing taxonomic nomenclature. We compared four major 16S databases (SILVA, Greengenes2, RefSeq, and MIMt) using 69 mock communities and the DADA2 analysis pipeline for microbial genus-level profiling. Database size was significantly reduced after cleaning: SILVA reduced from 452,055 to 291,733 sequences, Greengenes2 from 337,506 to 277,982 sequences, MIMt from 48,749 to 34,734 sequences, and RefSeq from 27,376 to 25,970 sequences. Greengenes2, MIMt, and RefSeq, which exhibited comparable performance, consistently outperformed SILVA in recall, precision, and abundance estimation accuracy across all sample types. Cleaning SILVA improved computational efficiency by up to 50% while maintaining classification performance. We provide a benchmarking framework with the cleaned databases as resources for accurate 16S rRNA analysis and profiling.

bioinformatics↗

bioRxiv2025

resLens: genomic language models to enhance antibiotic resistance gene detection

The rise of antibiotic resistance necessitates advanced tools to detect and analyze antibiotic resistance genes (ARGs). We present resLens, a family of genomic language models that leverage latent genomic representations to enhance ARG detection and analysis. Unlike alignment-based methods constrained by reference databases, resLens fine-tunes a pre-trained DNA language model on curated ARG datasets, achieving competitive or superior performance in classifying resistance genes across multiple evaluation scenarios, including when ARGs exhibit sequences and mechanisms of resistance dissimilar to those in reference datasets.

bioinformatics↗

bioRxiv2025

Phylogenetically-informed crayfish conservation in the face of climate change

Crayfish are an ancient clade of freshwater decapods that play vital and diverse ecological roles in the freshwater systems that they inhabit. One third of assessed crayfish species are threatened with extinction and 87% are highly sensitive to climate change. However, the extent to which the evolutionary history of crayfish is threatened, especially by climate change, remains unclear. To address this, we produced a phylogenetically-informed species prioritisation for the conservation of crayfish and explored the consequences of projected climate change scenarios on crayfish phylogenetic diversity. We used the EDGE2 metric to highlight 70 priority species for conservation. Their combined threatened evolutionary history accounts for 40.7% of the expected phylogenetic diversity loss for all 673 crayfish. Of these priority species, 45.7% are vulnerable to climate change, with the majority (32 species) located in Australia. Under a high warming scenario, individual species that are more evolutionarily distinct are projected to be disproportionately affected. The extinction of climate change vulnerable species could cause the loss of up to 26.3% more phylogenetic diversity than the random expectation, equivalent to 409 million years of evolutionary history. Our results highlight the need to consider climate change effects on phylogenetic diversity to fully understand its impact on biodiversity. If actions were taken to avoid a worst-case warming scenario in favour of an intermediate scenario, we could safeguard 251 million years of unique crayfish evolutionary history.

ecology↗

bioRxiv2025

seqLens: optimizing language models for genomic predictions

Understanding evolutionary variation in genomic sequences through the lens of language modeling has the potential to revolutionize biological research. Yet to maximize the utility of language modeling in genomics, we must overcome computational challenges in tokenization and model architecture adapted to diverse genomic features across evolutionary timescales. In this study, we investigated key elements in genomic language modeling (gLM), including tokenization, pretraining datasets, fine-tuning approaches, pooling methods, and domain adaptation, and applied the language models to diverse genomic data. We gathered two evolutionarily distinct pretraining datasets: one consisting of 19,551 reference genomes, including over 18,000 prokaryotic genomes (115B nucleotides) and the remainder eukaryotic genomes, and another more balanced dataset with 1,354 genomes, including 1,166 prokaryotic and 188 eukaryotic reference genomes (180B nucleotides). We trained five byte-pair encoding tokenizers and pretrained 52 gLMs, systematically comparing different architectures, hyperparameters, and classification heads. We introduce seqLens, a family of models based on disentangled attention with relative positional encoding, which outperforms relatively similar-sized models in 13 of 19 benchmarking phenotypic predictions. We further explore continual pretraining, domain adaptation, and parameter-efficient fine-tuning methods to assess trade-offs between computational efficiency and accuracy. Our findings demonstrate that relevant pretraining data significantly boost performance, alternative pooling techniques can enhance classification, tokenizers with larger vocabulary sizes negatively impact generalization, and gLMs are capable of understanding evolutionary relationships. These insights provide a foundation for optimizing genomic language models for identifying diverse evolutionary genomic features and improving genome annotations.

bioinformatics↗

bioRxiv2024

Discovering genotype-phenotype relationships with machine learning and the Visual Physiology Opsin Database (VPOD)

BackgroundPredicting phenotypes from genetic variation is foundational for fields as diverse as bioengineering and global change biology, highlighting the importance of efficient methods to predict gene functions. Linking genetic changes to phenotypic changes has been a goal of decades of experimental work, especially for some model gene families including light-sensitive opsin proteins. Opsins can be expressed in vitro to measure light absorption parameters, including {lambda}max - the wavelength of maximum absorbance - which strongly affects organismal phenotypes like color vision. Despite extensive research on opsins, the data remain dispersed, uncompiled, and often challenging to access, thereby precluding systematic and comprehensive analyses of the intricate relationships between genotype and phenotype. ResultsHere, we report a newly compiled database of all heterologously expressed opsin genes with {lambda}max phenotypes called the Visual Physiology Opsin Database (VPOD). VPOD_1.0 contains 864 unique opsin genotypes and corresponding {lambda}max phenotypes collected across all animals from 73 separate publications. We use VPOD data and deepBreaks to show regression-based machine learning (ML) models often reliably predict {lambda}max, account for non-additive effects of mutations on function, and identify functionally critical amino acid sites. ConclusionThe ability to reliably predict functions from gene sequences alone using ML will allow robust exploration of molecular-evolutionary patterns governing phenotype, will inform functional and evolutionary connections to an organisms ecological niche, and may be used more broadly for de-novo protein design. Together, our database, phenotype predictions, and model comparisons lay the groundwork for future research applicable to families of genes with quantifiable and comparable phenotypes. Key PointsO_LIWe introduce the Visual Physiology Opsin Database (VPOD_1.0), which includes 864 unique animal opsin genotypes and corresponding {lambda}max phenotypes from 73 separate publications. C_LIO_LIWe demonstrate that regression-based ML models can reliably predict {lambda}max from gene sequence alone, predict non-additive effects of mutations on function, and identify functionally critical amino acid sites. C_LIO_LIWe provide an approach that lays the groundwork for future robust exploration of molecular-evolutionary patterns governing phenotype, with potential broader applications to any family of genes with quantifiable and comparable phenotypes. C_LI

bioinformatics↗

bioRxiv2022

Convergent adaptation of true crabs (Decapoda: Brachyura) to a gradient of terrestrial environments

For much of terrestrial biodiversity, the evolutionary pathways of adaptation from marine ancestors are poorly understood, and have usually been viewed as a binary trait. True crabs, the decapod crustacean infraorder Brachyura, comprise over 7,600 species representing a striking diversity of morphology and ecology, including repeated adaptation to non-marine habitats. Here, we reconstruct the evolutionary history of Brachyura using new and published sequences of 10 genes for 344 tips spanning 88 of 109 brachyuran families. Using 36 newly vetted fossil calibrations, we infer that brachyurans most likely diverged in the Triassic, with family-level splits in the late Cretaceous and early Paleogene. By contrast, the root age is underestimated with automated sampling of 328 fossil occurrences explicitly incorporated into the tree prior, suggesting such models are a poor fit under heterogeneous fossil preservation. We apply recently defined trait-by-environment associations to classify a gradient of transitions from marine to terrestrial lifestyles. We estimate that crabs left the marine environment at least seven and up to 17 times convergently, and returned to the sea from non-marine environments at least twice. Although the most highly terrestrial- and many freshwater-adapted crabs are concentrated in Thoracotremata, Bayesian threshold models of ancestral state reconstruction fail to identify shifts to higher terrestrial grades due to the degree of underlying change required. Lineages throughout our tree inhabit intertidal and marginal marine environments, corroborating the inference that the early stages of terrestrial adaptation have a lower threshold to evolve. Our framework and extensive new fossil and natural history datasets will enable future comparisons of non-marine adaptation at the morphological and molecular level. Crabs provide an important window into the early processes of adaptation to novel environments, and different degrees of evolutionary constraint that might help predict these pathways.

evolutionary biology↗

bioRxiv2022

Major revisions in pancrustacean phylogeny with recommendations for resolving challenging nodes

The clade Pancrustacea, comprising crustaceans and hexapods, is the most diverse group of animals on earth, containing over 80% of animal species. It has been the subject of several recent phylogenomic analyses, but despite analyzing hundreds of genes, relationships within Pancrustacea show a notable lack of stability. Here, the phylogeny is estimated with expanded taxon sampling, particularly of malacostracans, using a precise tree-based approach to infer orthology. Our results show that small changes in taxon sampling have a large impact on phylogenetic estimation. By analyzing only shared orthologs between two slightly different taxon sets, we show that the differences in the resulting topologies are due to the effects of taxon sampling on the phylogenetic reconstruction method, not on ortholog identification. We compare trees resulting from our phylogenomic analyses with those from the literature to explore the large tree space of pancrustacean phylogenetic hypotheses and find that statistical topology tests reject the previously published trees in favor of the ML trees produced here. Our results reject several clades including Caridoida, Eucarida, Multicrustacea, Vericrustacea, and Syncarida. We recover a novel relationship between decapods, euphausiids, and syncarids that we refer to as the Syneucarida. With denser taxon sampling, we find Stomatopoda sister to this clade, which we name Stomatocaridea, dividing Malacostraca into three clades: Leptostraca, Peracarida, and Stomatocaridea. A new Bayesian divergence time estimation is conducted using 13 vetted fossils. We review our results in the context of other pancrustacean phylogenetic hypotheses and highlight the key taxa to sample in future studies.

evolutionary biology↗

bioRxiv2022

Metagenomic profiling pipelines improve taxonomic classification for 16S amplicon sequencing data

Most experiments studying bacterial microbiomes rely on the PCR amplification of all or part of the gene for the 16S rRNA subunit, which serves as a biomarker for identifying and quantifying the various taxa present in a microbiome sample. Several computational methods exist for analyzing 16S amplicon-based metagenomics. However, the most-used bioinformatics tools cannot produce quality genus-level or species-level taxonomic calls and may underestimate the degree to which these calls are possible. We used 16S sequencing data from mock bacterial communities to evaluate the sensitivity and specificity of several bioinformatics pipelines and genomic reference libraries used for microbiome analyses, concentrating on measuring the accuracy of species-level taxonomic assignments of 16S amplicon reads. We evaluated the tools Qiime 2, Mothur, PathoScope 2, and Kraken 2 in conjunction with reference libraries from Greengenes, Silva, Kraken, and RefSeq. Profiling tools were compared using publicly available mock community data from several sources, comprising 136 samples with varied species richness and evenness, several different amplified regions within the 16S gene, and both DNA spike-ins and cDNA from collections of plated cells. PathoScope 2 and Kraken 2, both tools designed for whole-genome metagenomics, outperformed Qiime 2 using the DADA2 plugin and Mothur, both of which are theoretically specialized for 16S analyses.

bioinformatics↗

bioRxiv2020

Diversity and genomic determinants of the microbiomes associated with COVID-19 and non-COVID respiratory diseases

The novel coronavirus disease 2019 (COVID-19) is a rapidly emerging and highly transmissible disease caused by the Severe Acute Respiratory Syndrome CoronaVirus-2 (SARS-CoV-2). Understanding the microbiomes associated with the upper respiratory tract infection (URTI), chronic obstructive pulmonary disease (COPD) and COVID-19 diseases has clinical interest. We hypothesized that the diversity of microbiome compositions and their genomic features are associated with different pathological conditions of these human respiratory tract diseases (COVID-19 and non-COVID; URTI and COPD). To test this hypothesis, we analyzed 21 whole metagenome sequences (WMS) including eleven COVID-19 (BD = 6 and China = 5), six COPD (UK = 6) and four URTI (USA = 4) samples to unravel the diversity of microbiomes, their genomic features and relevant metabolic functions. The WMS data mapped to 534 bacterial, 60 archaeal and 61 viral genomes with distinct variation in the microbiome composition across the samples (COVID-19>COPD>URTI). Notably, 94.57%, 80.0% and 24.59% bacterial, archaeal and viral genera shared between the COVID-19 and non-COVID samples, respectively, however, the COVID-19 related samples had sole association with 16 viral genera other than SARS-CoV-2. Strain-level virome profiling revealed 660 and 729 strains in COVID-19 and non-COVID sequence data, respectively and of them 34.50% strains shared between the conditions. Functional annotation of metagenomics sequences of thevCOVID-19 and non-COVID groups identified the association of several biochemical pathways related to basic metabolism (amino acid and energy), ABC transporters, membrane transport, replication and repair, clustering-based subsystems, virulence, disease and defense, adhesion, regulation of virulence, programmed cell death, and primary immunodeficiency. We also detected 30 functional gene groups/classes associated with resistance to antibiotics and toxic compounds (RATC) in both COVID-19 and non-COVID microbiomes. Furthermore, a predominant higher abundance of cobalt-zinc-cadmium resistance (CZCR) and multidrug resistance to efflux pumps (MREP) genes were detected in COVID-19 metagenome. The profiles of microbiome diversity and associated microbial genomic features found in both COVID-19 and non-COVID (COPD and URTI) samples might be helpful for developing the microbiome-based diagnostics and therapeutics for COVID-19 and non-COVID respiratory diseases. However, future studies might be carried out to explore the microbiome dynamics and the cross-talk between host and microbiomes employing larger volume of samples from different ethnic groups and geoclimatic conditions.

genomics↗

bioRxiv2020

COVID-19 Biomarkers in research: Extension of the OncoMX cancer biomarker data model to capture biomarker data from other diseases.

Scientists, medical researchers, and health care workers have mobilized worldwide in response to the outbreak of COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; SCoV2). Preliminary data have captured a wide range of host responses, symptoms, and lingering problems post-recovery within the human population. These variable clinical manifestations suggest differences in influential factors, such as innate and adaptive host immunity, existing or underlying health conditions, co-morbidities, genetics, and other factors. As COVID-19-related data continue to accumulate from disparate groups, the heterogeneous nature of these datasets poses challenges for efficient extrapolation of meaningful observations, hindering translation of information into clinical applications. Attempts to utilize, analyze, or combine biomarker datasets from multiple sources have shown to be inefficient and complicated, without a unifying resource. As such, there is an urgent need within the research community for the rapid development of an integrated and harmonized COVID-19 Biomarker Knowledgebase. By leveraging data collection and integration methods, backed by a robust data model developed to capture cancer biomarker data we have rapidly crowdsourced the collection and harmonization of COVID-19 biomarkers. Our resource currently has 138 unique biomarkers. We found multiple instances of the same biomarker substance being suggested as multiple biomarker types during our extensive cross-validation and manual curation. As a result, our Knowledgebase currently has 265 biomarker type combinations. Every biomarker entry is made comprehensive by bringing in together ancillary data from multiple sources such as biomarker accessions (canonical UniProtKB accession, PubChem Compound ID, Cell Ontology ID, Protein Ontology ID, NCI Thesaurus Code, and Disease Ontology ID), BEST biomarker category, and specimen type (Uberon Anatomy Ontology) unified with ontology standards. Our preliminary observations show distinct trends in the collated biomarkers. Most biomarkers are related to the immune system (SAA,TNF-{propto}, and IP-10) or coagulopathies (D-dimer, antithrombin, and VWF) and a few have already been established as cancer biomarkers (ACE2, IL-6, IL-4 and IL-2). These trends align with proposed hypotheses of clinical manifestations compounding the complexity of COVID-19 pathobiology. We explore these trends as we put forth a COVID-19 biomarker resource that will help researchers and diagnosticians alike. All biomarker data are freely available from https://data.oncomx.org/covid19.

bioinformatics↗

bioRxiv2020

Microbiome Dynamics of Bovine Mastitis Progression and Genomic Determinants

The milk of lactating cows presents a complex ecosystem of interconnected microbial communities which can impose a significant influence on the pathophysiology of mastitis. Previously, we reported the alteration of microbiome (bacteria, archaea, virus) composition between clinical mastitis (CM) and healthy (H) milk. We hypothesized possible dynamic shifts of microbiome compositions with the progress of different pathological states of mastitis (CM, Recurrent CM; RCM, Subclinical Mastitis; SCM) determined by its favoring genomic potentials. To evaluate this hypothesis, we employed whole metagenome sequencing (WMS) in 20 milk samples (CM = 5, RCM = 6, SCM = 4, H = 5) to unravel the microbiome dynamics, interrelation, and relevant metabolic functions. PathoScope (PS) and MG-RAST (MR) analyses mapped the WMS data to 442 bacterial, 58 archaeal and 48 viral genomes with distinct variation in microbiome composition and abundances across these metagenomes (CM>H>RCM>SCM). PS analysis identified 385, 65, 80 and 144 bacterial strains in CM, RCM, SCM, and H milk, respectively, with an inclusion of 67.19% previously unreported opportunistic strains in mastitis metagenomes. Moreover, MR detected 56, 13, 9 and 46 archaeal, and 40, 24, 11 and 37 viral genera in CM, RCM, SCM and H-milk metagenomes, respectively. The CM-microbiomes had closest association with RCM-microbiomes followed by SCM, and H-microbiomes. Furthermore, we identified 333, 304, 183 and 50 virulence factors-associated genes (VFGs), and 48, 31, 11 and 6 antibiotic resistance genes (AGRs) in CM, RCM, SCM, and H-microbiomes, respectively, showing a significant correlation between the relative abundances of VFGs (p = 0.001), ARGs (p = 0.0001), and associated bacterial taxa. We also detected correlated variations in the presence and abundance of several metabolic functional genes related to bacterial colonization, proliferation, chemotaxis, motility and invasion, oxidative stress, virulence and pathogenicity, phage integration and excision, biofilm-formation, and quorum-sensing to be associated with different episodes of mastitis. Therefore, profiling the dynamics of microbiome in different states of mastitis, concurrent VFGs, ARGs, and genomic functional correlations will contribute to developing microbiome-based diagnostics and therapeutics for bovine mastitis, and carries significant implications on curtailing the economic fallout from this disease.

microbiology↗

bioRxiv2020

Deep ancestral introgression shapes evolutionary history of dragonflies and damselflies

Introgression is arguably one of the most important biological processes in the evolution of groups of related species, affecting at least 10% of the extant species in the animal kingdom. Introgression reduces genetic divergence between species, and in some cases can be highly beneficial, facilitating rapid adaptation to ever-changing environmental pressures. Introgression also significantly impacts inference of phylogenetic species relationships where a strictly binary tree model cannot adequately explain reticulate net-like species relationships. Here we use phylogenomic approaches to understand patterns of introgression along the evolutionary history of a unique, non-model insect system: dragonflies and damselflies (Odonata). We demonstrate that introgression is a pervasive evolutionary force across various taxonomic levels within Odonata. In particular, we show that the morphologically "intermediate" species of Anisozygoptera (one of the three primary suborders within Odonata besides Zygoptera and Anisoptera), which retain phenotypic characteristics of the other two suborders, experienced high levels of introgression likely coming from zygopteran genomes. Additionally, we found evidence for multiple cases of deep inter-superfamilial ancestral introgression.

evolutionary biology↗

bioRxiv2020

Exon probe sets and bioinformatics pipelines for all levels of fish phylogenomics

Exon markers have a long history of use in phylogenetics of ray-finned fishes, the most diverse clade of vertebrates with more than 35,000 species. As the number of published genomes increases, it has become easier to test exons and other genetic markers for signals of ancient duplication events and filter out paralogs that can mislead phylogenetic analysis. We present seven new probe sets for current target-capture phylogenomic protocols that capture 1,104 exons explicitly filtered for paralogs using gene trees. These seven probe sets span the diversity of teleost fishes, including four sets that target five hyper-diverse percomorph clades which together comprise ca. 17,000 species (Carangaria, Ovalentaria, Eupercaria, and Syngnatharia + Pelagiaria combined). We additionally included probes to capture exon markers that have been commonly used in fish phylogenetics, despite some being flagged for paralogs, to facilitate integration of old and new molecular phylogenetic matrices. We tested these probes and merged new exon-capture sequence data into an existing data matrix of 1,105 exons and 300 ray-finned fish species. We provide an optimized bioinformatics pipeline to assemble exon capture data from raw reads to alignments for downstream analysis. We show that loci with known paralogs are at risk of assembling duplicated sequences with target-capture, but we also assembled many useful orthologous sequences. These probe sets are a valuable resource for advancing fish phylogenomics because they can be easily extracted from increasingly available whole genome and transcriptome datasets, and also may be integrated with existing PCR-based exon and mitochondrial datasets.

evolutionary biology↗

bioRxiv2019

Resistome diversity in bovine clinical mastitis microbiome, a signature concurrence

The bovine clinical mastitis (CM) milk is a large reservoir for diverse groups of resistomes, which play important roles in the pathogenesis of mastitis, but little is known about the concurrence of CM microbiome signature and its associated resistomes. Here we deciphered the total resistance (antibiotics and metals resistance, biofilm formation, quorum sensing) present in CM microbiome using whole metagenome sequencing (WMS) and in vitro cultural approaches. Significant correlation (p=0.001) was found between the resistome diversity and microbiome signature. We identified the strain-level microbiome diversity in four cattle breeds, with microbiome composition represented by the phyla Proteobacteria, Bacteroidetes, Firmicutes, Actinobacteria and Fusobacteria (contributing to >95.0% of total strains). However, the resistome diversity did not vary significantly (p=0.692) across the microbiomes of cattle breeds. The in vitro investigation showed that biofilm producing CM pathogens were resistant to most of the conventional antibiotics used for CM treatment, whereas these pathogens remained sensitive to five heavy metals (Cr, Co, Ni, Cu, Zn) at varying concentrations. We also found association of some genomic functional potentials such as bacterial flagellar movement and chemotaxis, regulation and cell signaling, phages-prophages, transposable elements, plasmids and oxidative stress in the pathophysiology of bovine CM. These findings of rapid and reliable identification of CM microbiomes and associated resistomes will help improve the optimization of therapeutic schemes involving antibiotics and metals usage in the prevention and control programs of bovine CM.

microbiology↗

bioRxiv2019

Evaluation of haplotype callers for next-generation sequencing of viruses

Currently, the standard practice for assembling next-generation sequencing (NGS) reads of viral genomes is to summarize thousands of individual short reads into a single consensus sequence, thus confounding useful intra-host diversity information for molecular phylodynamic inference. It is hypothesized that a few viral strains may dominate the intra-host genetic diversity with a variety of lower frequency strains comprising the rest of the population. Several software tools currently exist to convert NGS sequence variants into haplotypes. However, previous studies suggest that current approaches of haplotype reconstruction greatly underestimate intra-host diversity. Here, we tested twelve NGS haplotype reconstruction methods using viral populations simulated under realistic evolutionary dynamics. Parameters for the simulated data spanned known fast evolving viruses (e.g., HIV-1) diversity estimates to test the limits of the haplotype reconstruction methods and ensured coverage of predicted intra-host viral diversity levels. Using those parameters, we simulated HIV-1 viral populations of 216-1,185 haplotypes per host at a frequency <7%. All twelve investigated haplotype callers showed variable performance and produced drastically different results that were mainly driven by differences in mutation rate and, to a lesser extent, in effective population size. Most methods were able to accurately reconstruct haplotypes when genetic diversity was low. However, under higher levels of diversity (e.g., those seen intra-host HIV-1 infections), haplotype reconstruction accuracy was highly variable and, on average, poor. High diversity levels led to severe underestimation of, with a few tools greatly overestimating, the true number of haplotypes. PredictHaplo and PEHaplo produced estimates close to the true number of haplotypes, although their haplotype reconstruction accuracy was worse than that of the other ten tools. We conclude that haplotype reconstruction from NGS short reads is unreliable due to high genetic diversity of fast-evolving viruses. Local haplotype reconstruction of longer reads to phase variants may provide a more reliable estimation of viral variants within a population.\n\nHighlightsO_LIHaplotype callers for NGS data vary greatly in their performance.\nC_LIO_LIHaplotype callers performance is mainly determined by mutation rate.\nC_LIO_LIHaplotype callers performance is less sensitive to effective population size.\nC_LIO_LIMost haplotype callers perform well with low diversity and poorly with high diversity.\nC_LIO_LIPredictHaplo performs best if genetic diversity is in the range of HIV diversity.\nC_LI

bioinformatics↗

bioRxiv2019

Environmental DNA survey captures patterns of fish and invertebrate diversity across a tropical seascape

Accurate, rapid, and comprehensive biodiversity assessments are critical for investigating ecological processes and supporting conservation efforts. Environmental DNA (eDNA) surveys show promise as a way to effectively characterize fine-scale patterns of community composition, but most studies to date have evaluated its effectiveness in single habitats and for conspicuous taxonomic groups in temperate ecosystems. We tested whether a single PCR survey of eDNA in seawater using a broad metazoan primer could identify differences in community composition between five adjacent habitats at 19 sites across a tropical Caribbean bay in Panama. We paired this effort with visual fish surveys to compare methods for a conspicuous taxonomic group. eDNA revealed a tremendous diversity of animals (8,586 operational taxonomic units), including many small taxa that would be undetected in traditional in situ surveys. Fish comprised only 0.07% of the taxa detected by a broad COI primer, yet included 43 species not observed in the visual survey. eDNA revealed significant differences in fish and invertebrate community composition across adjacent habitats and areas of the bay driven in part by taxa known to be habitat-specialists or tolerant to wave action. Our results demonstrate the ability of broad eDNA surveys to identify biodiversity patterns in the ocean.

ecology↗